Sign In

Communications of the ACM

71 - 80 of 3,913 for bentley

Density-based Algorithms for Big Data Clustering Using MapReduce Framework: A Comprehensive Study

Clustering is used to extract hidden patterns and similar groups from data. Therefore, clustering as a method of unsupervised learning is a crucial technique for big data analysis owing to the massive number of unlabeled objects involved. Density-based algorithms have attracted research interest, because they help to better understand complex patterns in spatial datasets that contain information about data related to co-located objects. Big data clustering is a challenging task, because the volume of data increases exponentially. However, clustering using MapReduce can help answer this challenge. In this context, density-based algorithms in MapReduce have been largely investigated in the past decade to eliminate the problem of big data clustering. Despite the diversity of the algorithms proposed, the field lacks a structured review of the available algorithms and techniques for desirable partitioning, local clustering, and merging. This study formalizes the problem of density-based clustering using MapReduce, proposes a taxonomy to categorize the proposed algorithms, and provides a systematic and comprehensive comparison of these algorithms according to the partitioning technique, type of local clustering, merging technique, and exactness of their implementations. Finally, the study highlights outstanding challenges and opportunities to contribute to the field of density-based clustering using MapReduce.


Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations

In many types of dynamic interactive visualizations, it is often desired to interact with moving objects. Stopping moving objects can make selection easier, but pausing animated content can disrupt perception and understanding of the visualization. To address such problems, we explore selection techniques that only pause a subset of all moving targets in the visualization. We present various designs for controlling pause regions based on cursor trajectory or cursor position. We then report a dual-task experiment that evaluates how different techniques affect both target selection performance and contextual awareness of the visualization. Our findings indicate that all pause techniques significantly improved selection performance as compared to the baseline method without pause, but the results also show that pausing the entire visualization can interfere with contextual awareness. However, the problem with reduced contextual awareness was not observed with our new techniques that only pause a limited region of the visualization. Thus, our research provides evidence that region-limited pause techniques can retain the advantages of selection in dynamic visualizations without imposing a negative effect on contextual awareness.


Efficient simulation of macroscopic molecular communication: the pogona simulator

Molecular communication in pipe networks is a novel technique for wireless data exchange. Simulating such networks accurately is difficult because of the complexity of fluid dynamics at centimeter scales, which existing molecular communication simulators do not model. The new simulator we present combines computational fluid dynamics simulation and particle movement predictions. It is optimized to be computationally efficient while offering a high degree of adaptability to complex fluid flows in larger pipe networks. We validate it by comparing the simulation with experimental results obtained in a real-world testbed.


Shining light on molecular communication

Molecules and combinations of molecules are the natural communication currency of microbes; microbes have evolved and been engineered to sense a variety of compounds, often with exquisite sensitivity. The availability of microbial biosensors, combined with the ability to genetically engineer biological circuits to process information, make microbes attractive bionanomachines for propagating information through molecular communication (MC) networks. However, MC networks built entirely of biological components suffer a number of limitations. They are extremely slow due to processing and propagation delays and must employ simple algorithms due to the still limited computational capabilities of biological circuits. In this work, we propose a hybrid bio-electronic framework which utilizes biological components for sensing but offloads processing and computation to traditional electronic systems and communication infrastructure. This is achieved by using tools from the burgeoning field of optogenetics to trigger biosensing through an optoelectronic interface, alleviating the need for computation and communication in the biological domain.


NanoCom '20: Proceedings of the 7th ACM International Conference on Nanoscale Computing and Communication

The main goals of the 7th ACM International Conference on Nanoscale Computing and Communication (ACM NanoCom 2020), are to increase the visibility of this growing research area to the wider computing and communication research communities as well as bring together researchers from diverse disciplines that can foster and develop new paradigms for nanoscale devices. Due to the highly inter-disciplinary nature of this field of research, the conference aims to attract researchers and academics from various areas of study such as electrical and electronic engineering, computer science, biology, chemistry, physics, mathematics, bioengineering, biotechnology, materials science, nanotechnology, who have an interest in computing and communications at the nanoscale.


The use of AI in public services: results from a preliminary mapping across the EU

Artificial Intelligence is a new set of technologies which has grasped the attention of many in society due to its potential. These technologies could also provide great benefits to public administrations when adopted. This paper acts as a first landscaping analysis to indicate, classify and understand current AI-implementations in public services. By conducting a desk research based on available documents describing AI projects, 85 AI applications in the public sector in selected European countries have been identified and reviewed. The preliminary analysis suggests that most AI initiatives are started with efficiency goals in mind, and they occur mainly in the general public service policy area. Findings of this preliminary landscape analysis set the basis for further more in depth research and recommendations for policy.


A Generalized Robinson-Foulds Distance for Clonal Trees, Mutation Trees, and Phylogenetic Trees and Networks

Cancer evolution is often modeled by clonal trees (whose nodes are labeled by multiple somatic mutations) or mutation trees (where nodes are labeled by single somatic mutations). Clonal trees are generated from sequence data with different computational methods that may produce different clone phylogenies, rendering their analysis and comparison necessary to infer mutation order and clone origin during tumor progression. In this paper, we present a distance metric for multi-labeled trees that generalizes the Robinson-Foulds distance for phylogenetic trees, allows for a similarity assessment at much higher resolution, and can be applied to trees and networks with different sets of node labels. The generalized Robinson-Foulds distance can be computed in time quadratic in the size of the input multisets of multisets of node labels, and is a metric for clonal trees, mutation trees, phylogenetic trees, and several classes of phylogenetic networks.


GROOT: a real-time streaming system of high-fidelity volumetric videos

We present GROOT, a mobile volumetric video streaming system that delivers three-dimensional data to mobile devices for a fully immersive virtual and augmented reality experience. The system design for streaming volumetric videos should be fundamentally different from conventional 2D video streaming systems. First, the amount of data required to deliver the 3D volume is considerably larger than conventional videos with frames of 2D images, even compared to high-resolution 2D or 360° videos. Second, the 3D data representation, which encodes the surface of objects within the volume, is a sparse and unorganized data structure with varying scales, whereas a conventional video is composed of a sequence of images with the fixed-size 2D grid structure. GROOT is a streaming framework with a novel data structure that enables not only real-time transmission and decoding on mobile devices but also continuous on-demand user view adaptation. Specifically, we modify the conventional octree to introduce the independence of leaf nodes with minimal memory overhead, which enables parallel decoding of highly irregular 3D data. We also developed a suite of techniques to compress color information and filter out 3D points outside of a user's view, which efficiently minimizes the data size and decoding cost. Our extensive evaluation shows that GROOT achieves more stable but faster frame rates compared to any previous method to stream and visualize volumetric videos on mobile devices.


Predicting Monthly Pageview of Wikipedia Pages by Neighbor Pages

Predicting traffic has been important for websites' daily services. Developing efficient models for Wikipedia's page traffic would deepen our knowledge about people's behavior on Wikipedia and potentially for other crowdsourcing pages. The current project attempted to experiment with incorporating time series data from a linked page trying to improve the prediction accuracy of future traffic of a page. The current study experimented with three timeseries models. The baseline model uses the monthly traffic of 2019 of a page to predict the monthly traffic of January of 2020. The random neighbor model randomly selects a page which has a hyperlink to the focal page and uses the 2019 data of the focal page and the neighboring page to predict the monthly traffic of January of 2020. The similar neighbor model also uses data from the focal and a neighboring page, but the neighbor is selected based on its content similarity to the focal page. The results show that prediction with a similar neighbor model has better prediction performance than with the Random neighbor model on popular pages. The baseline model has the best performance with the smallest MSE, MAE, and MAPE, while the random neighbor model and similar neighbor model have much larger MSE than the Baseline model.


Use of Intelligent Voice Assistants by Older Adults with Low Technology Use

Voice assistants embodied in smart speakers (e.g., Amazon Echo, Google Home) enable voice-based interaction that does not necessarily rely on expertise with mobile or desktop computing. Hence, these voice assistants offer new opportunities to different populations, including individuals who are not interested or able to use traditional computing devices such as computers and smartphones. To understand how older adults who use technology infrequently perceive and use these voice assistants, we conducted a 3-week field deployment of the Amazon Echo Dot in the homes of seven older adults. While some types of usage dropped over the 3-week period (e.g., playing music), we observed consistent usage for finding online information. Given that much of this information was health-related, this finding emphasizes the need to revisit concerns about credibility of information with this new interaction medium. Although features to support memory (e.g., setting timers, reminders) were initially perceived as useful, the actual usage was unexpectedly low due to reliability concerns. We discuss how these findings apply to other user groups along with design implications and recommendations for future work on voice-user interfaces.